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Record W3025659588 · doi:10.1149/ma2020-014557mtgabs

Using Experiment and First-Principles to Assess Electrochemical Windows of Common Solid Electrolytes for Their Application in All Solid-State Lithium Batteries.

2020· article· en· W3025659588 on OpenAlexaff
Yasmine Benabed, Maxime Rioux, Steeve Rousselot, Geoffroy Hautier, Mickaël Dollé

Bibliographic record

VenueECS Meeting Abstracts · 2020
Typearticle
Languageen
FieldEngineering
TopicAdvanced Battery Materials and Technologies
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsLithium (medication)ElectrolyteElectrochemistryFast ion conductorMaterials scienceElectrodeSAFERNanotechnologyComputer scienceChemistry

Abstract

fetched live from OpenAlex

During the last decades, lithium batteries have been developed to power a growing number of portable applications and to meet the needs of an increasingly mobile society. Their industrial and commercial application has always occurred in two successive steps of equal importance: the discovery of new electrode materials and/or electrolytes, followed by their extensive optimisation. A new generation of lithium batteries has been recently developed to meet high expectations in terms of safety, stability and capacity: All-Solid-State Lithium Batteries (ASSLB), where the conventional liquid electrolyte (LiPF6 + EC/DEC) is replaced by a safer and more stable ceramic, polymer or glass solid electrolyte (SE). ASSLB are partly developed with the prospect of using high potential materials as positive electrode and lithium metal as negative electrode. This is only possible through SE stated large electrochemical windows. Nevertheless, values for these electrochemical windows are very divergent in the literature published through the last decades. Recently, several studies have come to specifically decry the frequent overestimation of SE electrochemical stabilities 1,2. Establishing a robust procedure to determine SE real electrochemical windows has become detrimental. Our work is focused on developing a combined theoretical and experimental approach to better assess the electrochemical stability of widely used SE such as Li1.3Al0.3Ti1.7(PO4)3, Li1.5Al0.5Ti1.5(PO4)3 and LiLaTi2O6. In this presentation, we shed light on the importance of selecting the right experimental setup and explore the link between experimental and interpreted thermodynamic results. 1) Y. Tian, T. Shi, W. Richards, J. Li, J. Kim, S-H. Bo, G. Ceder. Energy Environ. Sci., 2017, 10, 1150 2)Z. Zhang, Y. Shao, B. Lotsch, Y-S. Hu, H. Li, J. Janek, L. Nazar, C-W. Nan, J. Maier, M. Armand, L. Chen. Energy Environ. Sci. 2018,11, 1945-1976.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.004
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.002
Scholarly communication0.0020.003
Open science0.0020.002
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0030.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.049
GPT teacher head0.293
Teacher spread0.243 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2020
Admission routes1
Has abstractyes

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